Part II bridges the gap between biological observations and computational analysis. We introduce the mathematical languages and data science tools essential for both neuroscience research and AI development.
From information theory to causal inference, from statistical modeling to Bayesian decision-making, these chapters provide the quantitative foundations for rigorous NeuroAI research. You’ll learn to formalize neural computations, analyze experimental data, and build principled models of brain function.
Key Themes
Information Theory: Quantifying neural coding efficiency and capacity
Data Science Pipeline: From raw recordings to scientific insights
Causality: Distinguishing correlation from causal relationships
Statistical Modeling: Generalized linear models for neural data
Bayesian Inference: Optimal decision-making under uncertainty
Chapters in This Part
Chapter 7: Information Theory Essentials Entropy, mutual information, and the mathematics of neural coding
Chapter 8: The Neuro-AI Data Science Pipeline: From Raw Data to Insight Processing, analyzing, and visualizing neural recordings
Chapter 9: Causal Inference in NeuroAI Granger causality, interventions, and causal graphs
Chapter 10: Model Fitting and Generalized Linear Models Statistical frameworks for understanding neural responses
Chapter 11: Bayesian Decision Making Probabilistic inference and optimal behavior
What You’ll Learn
By the end of Part II, you will understand:
- ✓ How to quantify information in neural signals
- ✓ Data preprocessing and analysis for neural recordings
- ✓ Methods for inferring causal relationships in brain data
- ✓ Statistical models relating stimuli to neural responses
- ✓ Bayesian approaches to perception and decision-making
- ✓ The mathematical foundations underlying neural computations
Mathematics is the language that transforms neural observations into computational understanding.